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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Microstructural Characterization and High Strain Rate Plastic Flow Behavior of SMAW Armox500T Steel Joints from Spherical Indentation Experiments

Static indentation and uniaxial compression tests have been conducted on Armox500T steel and its weldments to predict the constraint factor (CF), i.e., ratio of Meyer’s hardness to uniaxial flow stress. Series of dynamic indentation experiments were carried out at impact velocities ranging from 5 to 300 m/s to estimate dynamic hardness as a function of average strain. Subsequently, dynamic indentation (DI) test data and CF determined under static indentation conditions have been used to study the high strain rate plastic flow behavior of Armox500T weldments in comparison with the base metal. It was observed that flow stress for Armox500T and its weldments under dynamic loading conditions (10{sup 4} s{sup −1}) are significantly higher than flow stress measured under static loading conditions (10{sup −3} s{sup −1}). The plastic flow behavior computed from DI is in good agreement with the data evaluated through conventional split Hopkinson pressure bar technique. Further, the study of the microstructure of base metal and weldments by optical microscopy and SEM revealed a considerable variation in the microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Land cover change-induced decline in terrestrial gross primary production over the conterminous United States from 2001 to 2016

As one of the most dynamic aspects of global environmental change, land cover change (LCC) has a profound impact on terrestrial carbon sequestration. However, LCC-induced carbon fluxes are still the most uncertain terms in global and regional carbon budgets. Ecosystem gross primary production (GPP) is the total carbon uptake by vegetation through photosynthesis, serving as a major control on ecosystem function and land carbon balance during and after the modification of the land surface. However, accurately capturing LCC-induced GPP changes requires both high-quality land cover data and controlling for variation driven by other environmental factors such as climate. In this study, we comprehensively examined the effects of LCC on annual GPP trends over the conterminous United States (CONUS) from 2001 to 2016 using the USGS National Land Cover Database, a remote sensing-driven ecosystem model, and the Google Earth Engine cloud computing platform. We designed a series of model experiments to identify LCC effects on GPP by controlling climate effects. During the study period, LCC exerted a strong negative effect on total GPP across the CONUS ([-2.2, -1.8] Tg C yr -2 ), while climate had smaller positive effects ([0.17, 0. 92] Tg C yr -2 ). The LCC-induced reduction of GPP was mainly caused by net forest loss ([-1.98, -1.39] Tg C yr -2 ) and urban expansion ([-2.03, -1.92] Tg C yr -2 ), but was partially offset by increases in crop area ([+0.66, +0.79] Tg C yr -2 ). Ensemble simulations from TRENDY did not capture the strong negative LCC influences on GPP, likely due to limitations of the adopted land use/cover data. Overall, our study provides a novel perspective on LCC-induced GPP changes, which could help to improve our understanding of ecosystem function changes and constrain the estimation of land carbon balance in the context of anthropogenic activity and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Laser-diagnostic mapping of temperature and soot statistics in a 2-m diameter turbulent pool fire

We present spatial profiles of temperature and soot-volume-fraction statistics from a sooting 2-m base diameter turbulent pool fire, burning a 10%-toluene / 90%-methanol fuel mixture. Dual-pump coherent anti-Stokes Raman scattering and laser-induced incandescence are utilized to obtain radial profiles of temperature and soot probability density functions (pdf) as well as estimates of temperature/soot joint statistics at three vertical heights above the surface of the methanol/toluene fuel pool. Results are presented both in the fuel vapor-dome region at ¼ base diameter and in the actively burning region at ½ and ¾ diameters above the fuel surface. The spatial evolution of the soot and temperature pdfs is discussed and profiles of the temperature and soot mean and rms statistics are provided. Joint temperature/soot statistics are presented as spatially resolved conditional averages across the fire plume, and in terms of a joint pdf obtained by including measurements from multiple spatial locations.

42 ENGINEERING↗
DOE OSTI2020

Hedonic Price Estimates of Lake Water Quality: Valued Attribute, Instrumental Variables, and Ecological-Economic Benefits

We estimate the effect of lake water quality on residential housing prices using cross-sectional data on water clarity from 113 lakes across the United States. An instrumental-variables approach is developed to address potential endogeneity bias in the water-clarity variable. Three lake-based physical variables serve as instrumental variables: total nitrogen concentration in lake water, total phosphorus concentration in lake water, and water temperature near the lake surface. The econometric results include three methodological findings: the instruments are valid and strong; the ordinary-least-squares estimate of the coefficient on the water-clarity variable is unbiased; and water clarity is the attribute of water quality valued in the housing market. The estimated water-clarity effect shows that a one-tenth of a meter change in water clarity leads to a one-percent change in housing price, or an elasticity of 0.20 at mean clarity. Coupling this effect with estimates of an ecological production function, a lake-specific benefit index is developed that shows the effect on housing values of bringing lakes into compliance with the US Environmental Protection Agency's regional recommendations for phosphorous concentration in lake waters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Kilovoltage Intrafraction Monitoring for Prostate Intensity Modulated Arc Therapy: First Clinical Results

Purpose: Most linear accelerators purchased today are equipped with a gantry-mounted kilovoltage X-ray imager which is typically used for patient imaging prior to therapy. A novel application of the X-ray system is kilovoltage intrafraction monitoring (KIM), in which the 3-dimensional (3D) tumor position is determined during treatment. In this paper, we report on the first use of KIM in a prospective clinical study of prostate cancer patients undergoing intensity modulated arc therapy (IMAT). Methods and Materials: Ten prostate cancer patients with implanted fiducial markers undergoing conventionally fractionated IMAT (RapidArc) were enrolled in an ethics-approved study of KIM. KIM involves acquiring kV images as the gantry rotates around the patient during treatment. Post-treatment, markers in these images were segmented to obtain 2D positions. From the 2D positions, a maximum likelihood estimation of a probability density function was used to obtain 3D prostate trajectories. The trajectories were analyzed to determine the motion type and the percentage of time the prostate was displaced {>=}3, 5, 7, and 10 mm. Independent verification of KIM positional accuracy was performed using kV/MV triangulation. Results: KIM was performed for 268 fractions. Various prostate trajectories were observed (ie, continuous target drift, transient excursion, stable target position, persistent excursion, high-frequency excursions, and erratic behavior). For all patients, 3D displacements of {>=}3, 5, 7, and 10 mm were observed 5.6%, 2.2%, 0.7% and 0.4% of the time, respectively. The average systematic accuracy of KIM was measured at 0.46 mm. Conclusions: KIM for prostate IMAT was successfully implemented clinically for the first time. Key advantages of this method are (1) submillimeter accuracy, (2) widespread applicability, and (3) a low barrier to clinical implementation. A disadvantage is that KIM delivers additional imaging dose to the patient.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

An evaluation of tri-valent oxide (Cr 2 O 3 ) as a grain enlarging dopant for UO 2 nuclear fuels fabricated under reducing environment

A study was performed to evaluate the microstructure and crystallography of nominally 500–2000 Cr 2 O 3 -doped UO 2 fabricated in a temperature range of 1150–1750°C under reducing experimental conditions. We observed an increase in grain size of the samples with the increase in heat treating temperature as expected. For a given sintering temperature (1700–1750°C), an increase in the grain size was also observed with the increase in Cr 2 O 3 concentration up to a value of ~1000–1200 wppm. A decrease in fission gas release as a function of grain size was estimated for the Cr 2 O 3 -doped UO 2 samples assuming specified post-irradiation annealing conditions. A nearly linear decrease was obtained in the lattice parameter of the Cr 2 O 3 -doped UO 2 fcc phase with the increase in Cr 2 O 3 concentration, especially up to a nominal value of 1000 wppm. The lattice parameter decrease was also persistent with the increase in the average grain size as a result of addition of Cr 2 O 3 into the UO 2 lattice. An increase in the crystallite size and a decrease in the microstrain of the $fcc$ phase were observed with the increase in the average grain size of the samples, indicating a higher crystallinity of the Cr 2 O 3 -doped samples than that of the undoped UO 2 sample.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Optimization of nonlinear turbulence in stellarators

We present new stellarator equilibria that have been optimized for reduced turbulent transport using nonlinear gyrokinetic simulations within the optimization loop. The optimization routine involves coupling the pseudo-spectral GPU-native gyrokinetic code GX with the stellarator equilibrium and optimization code DESC . Since using GX allows for fast nonlinear simulations, we directly optimize for reduced nonlinear heat fluxes. To handle the noisy heat flux traces returned by these simulations, we employ the simultaneous perturbation stochastic approximation (SPSA) method that only uses two objective function evaluations for a simple estimate of the gradient. We show several examples that optimize for both reduced heat fluxes and good quasi-symmetry as a proxy for low neoclassical transport. Finally, we run full transport simulations using the T3D stellarator transport code to evaluate the changes in the macroscopic profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Ab Initio Molecular Dynamics of Uranium Incorporated in Goethite (α-FeOOH): Interpretation of X-ray Absorption Spectroscopy of Trace Polyvalent Metals

Incorporation of economically or environmentally consequential polyvalent metals into iron (oxyhydr)oxides has applications in environmental chemistry, remediation, and materials science. A primary tool for characterizing the local coordination environment of such metals, and therefore building models to predict their behavior, is extended X-ray absorption fine structure spectroscopy (EXAFS). Accurate structural information can be lacking, yet is required to constrain and inform data interpretation. In this regard, ab initio molecular dynamics (AIMD) was used to calculate the local coordination environment of minor amounts of U incorporated in the structure of goethite (α-FeOOH). U oxidation state (VI, V, and IV) and charge compensation scheme (CCS) were varied. Simulated trajectories were used to calculate the U LIII-edge EXAFS function and fit experimental EXAFS data for U incorporated into goethite under reducing conditions. Calculations that closely matched the U EXAFS of the well-characterized mineral uraninite (UO2), and constrained the S02 parameter to be 0.909, validated the approach. The results for the U-goethite system indicated that U(V) substituted for structural Fe(III) in octahedral uranate coordination. Charge balance was achieved by the loss of one structural proton coupled to injection of one electron into the solid (–1 H+, + 1 e-). The ability of AIMD to model higher-energy states thermally accessible at room temperature is particularly relevant for protonated systems such as goethite, where proton transfers between adjacent octahedra had a dramatic effect on the calculated EXAFS. Vibrational effects as a function of temperature were also estimated using AIMD, allowing separate quantification of thermal and configurational disorder. In summary, coupling AIMD structural modeling and EXAFS experiments enables modeling of the redox behavior of polyvalent metals that are incorporated in conductive materials such as iron (oxyhydr)oxides, with applications over a broad swath of chemistry and materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DONKEY: A Flexible and Accurate Algorithm for Clustering

We propose an accurate clustering algorithm suitable for the varied and multidimensional data sets that correspond to temporal snapshots from on-the-fly nonadiabatic trajectory-based simulations of photoexcited dynamics. The algorithm approximates the underlying probability density function using variable kernel density estimation, with local maxima corresponding to cluster centers. Each data point is then assigned to one of the maxima by employing a maximization procedure. Finally, clusters artificially separated by minor fluctuations in the probability density are merged. The algorithm does not require parameter tuning, which ensures flexibility and reduces the risk of bias. It is tested on several synthetic data sets, where it consistently outperforms conventional clustering algorithms. As a final example, the algorithm is applied to the excited dynamics of the norbornadiene ⇌ quadricyclane (C 7 H 8 ) molecular photoswitch, demonstrating how distinct reaction pathways can be identified.

algorithms↗
DOE OSTI2020

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Furthermore, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Towards Prediction of Non-Radiative Decay Pathways in Organic Compounds I: The Case of Naphthalene Quantum Yields

Many emerging technologies depend on human’s ability to control and manipulate the excited-state properties of molecular systems. These technologies include fluorescent labeling in biomedical imaging, light harvesting in photovoltaics, and electroluminescence in light-emitting devices. All of these systems suffer from non-radiative loss pathways that dissipate electronic energy as heat, which causes the overall system efficiency to be directly linked to quantum yield (Φ) of the molecular excited state. Unfortunately, Φ is very difficult to predict from first principles because the description of a slow non-radiative decay mechanism requires an accurate description of long-timescale excited-state quantum dynamics. In the present study, we introduce an efficient semi-empirical method of calculating the fluorescence quantum yield (Φ fl ) for molecular chromophores, which converts simple electronic energies computed using time-dependent density functional theory (TDDFT) into an estimate of Φ fl . As with all machine learning strategies, the algorithm needs to be trained on fluorescent dyes for which Φ fl ’s are known, so as to provide a black-box method which can later predict Φfl’s for chemically similar chromophores that have not been studied experimentally. Here, as a first illustration of how our proposed algorithm can be trained, we examine a family of 25 naphthalene derivatives. The simplest application of the energy gap law is found to be inadequate to explain the rates of internal conversion (IC) or intersystem crossing (ISC) – the electronic properties of at least one higher-lying electronic state (S n or T n ) or one far-from-equilibrium geometry are typically needed to obtain accurate results. Indeed, the key descriptors turn out to be the transition state between the Franck–Condon minimum and a distorted local minimum near an S 0 /S 1 conical intersection (which governs IC) and the magnitude of the spin-orbit coupling (which governs ISC). The resulting Φ fl ’s are predicted with reasonable accuracy (~ 22%), making our approach a promising ingredient for high-throughput screening and rational design of the molecular excited states with desired Φ fl ’s. We thus conclude that our model, while semi-empirical in nature, does in fact extract sound physical insight into the challenge of describing non-radiative relaxations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Coherent Spins in van der Waals Semiconductor GeS 2 at Ambient Conditions

Optically active spin defects in van der Waals (vdW) materials have emerged as versatile quantum sensors, enabling applications for a wide range of quantum phenomena in condensed matter systems. Their ease of exfoliation and compatibility with device integration make them promising candidates for future quantum technologies. Here we report the observation and room-temperature coherent control of ensemble spin defects in the high-temperature crystalline phase of germanium disulfide (β-GeS 2 ), a two-dimensional (2D) semiconductor with low nuclear spin density. The defects exhibit spin-1/2 behavior, and their dynamics can be explained by a weakly coupled spin-pair model. We implement dynamical decoupling techniques to extend the coherence time (T 2 ) by a factor of 20. Finally, we use density functional theory (DFT) calculations to estimate the structures and spin densities of two possible spin defect candidates. This work will help to expand the field of quantum sensing with spin defects in 2D materials.

2D materials↗
DOE OSTI2020

Emergence of unprecedented climate change in projected future precipitation

The future time of emergence when precipitation changes due to anthropogenic influences begins to continuously exceed the previous maximum value is defined as the ‘tipping year’ Historical experiments and future experiments simulated by state-of-the-art climate models were utilized. A total of 510,000 time series from year 1856 to 2095 were generated by sampling the natural internal variability in precipitation. The time evolutions of internal variability in the whole time period were estimated from the combination of past and future experiments with preindustrial control experiments. A large ensemble size enabled an estimation of the probability density function of the tipping year at each grid point, providing precise information on the uncertainty of the projection. The tipping year of average precipitation emerges earlier in high latitudes than in lower latitudes. In some regions in lower latitudes and mid-latitudes, the tipping year of intense precipitation emerges faster than that of average precipitation. The tipping years of average and intense precipitation are earlier for higher anthropogenic forcing scenarios than for lower scenarios. The global average of the tipping year for intense precipitation might be attributed to the enhancement of the thermodynamic effect (moisture) rather than the dynamic effect (vertical motion).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Gelation of Fmoc-diphenylalanine is a first order phase transition

We explore the gel transition of the aromatic dipeptide derivative molecule fluorenylmethoxycarbonyl-diphenylalanine (Fmoc-FF). The addition of water to a solution of Fmoc-FF in dimethyl sulfoxide (DMSO) results in increased attractions leading to self-assembly of Fmoc-FF molecules into a space-filling fibrous network. We provide evidence that gel formation is associated with a first order phase transition resulting in nucleation and growth of strongly anisotropic crystals with high aspect ratios. The strength of attraction between Fmoc-FF molecules as a function of water concentration is estimated from long-time self-diffusion measurements using 1H NMR diffusion-ordered spectroscopy (DOSY). The resulting phase behavior follows that observed for a wide range of other crystallizing nanoparticles and small molecules – a result consistent with the short-range nature of the intermolecular attractions. Furthermore, we use NMR to measure the rate of increase in the fraction of bound Fmoc-FF molecules after water is suddenly mixed into the system. We observe a lag time in the formation of the new phase indicative of the existence of a free energy barrier to the formation of a crystal nucleus of critical size. Furthermore, the application of classical nucleation theory for a cylindrical nucleus indicates that one-dimensional crystal growth is driven by an imbalance of the surface energies of the ends and sides of the fiber.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Studies of the fast ion energy spectra in TJ-II

The dynamics of the neutral beam injection fast ions in the TJ-II stellarator is studied in this paper from both the theoretical and experimental points of view. The code Integrator of Stochastic Differential Equations for Plasmas (ISDEP) is used to estimate the fast ion distribution function in 3D:1D in real space and 2D in velocity space, considering the 3D structure of TJ-II, the electrostatic potential, non turbulent collisional transport, and charge exchange losses. The results of ISDEP are compared with the experimental data from the compact neutral particle analyzer, which measures the outgoing neutral flux spectra in the energy range E Element-Of (1-45) keV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗